Adolescence is a critical developmental stage, yet millions of adolescents continue to face preventable mortality risks worldwide.
This report explores adolescent mortality patterns globally, using data from UNICEF datasets.
We analyze how gender, geographic region, and economic development influence mortality outcomes.
Gender Differences in Mortality (Top 10 Countries)
Mortality risks differ across genders.
The chart below shows the top 10 countries with the highest adolescent mortality rates, comparing male and female adolescents.
Insight:
Males generally experience higher mortality than females, though the gap varies by country.
African nations dominate the top 10 list, highlighting regional disparities.
Time Trends in Adolescent Mortality (Nigeria)
Analyzing mortality trends over time helps assess improvements or deteriorations in adolescent health.
Insight:
While Nigeria shows slight improvements over time, mortality rates remain significantly high compared to global averages.
Global Mortality Distribution
The global map below visualizes adolescent mortality rates across countries.
Code
# Fix country namesdata['country'] = data['country'].str.title()# Latest global datalatest_global_data = data[data['time_period'] == latest_year]# World mapfig = px.choropleth( latest_global_data, locations="country", locationmode="country names", color="obs_value", color_continuous_scale="Blues", title="Global Distribution of Adolescent Mortality Rates", labels={'obs_value': 'Mortality Rate (per 1,000)'})fig.show()
Insight:
Sub-Saharan Africa and parts of South Asia report the highest adolescent mortality rates, while Europe and North America report the lowest.
Mortality Patterns Across GDP Bands (Gender Split)
Grouping countries into GDP bands offers a clearer view of how economic development relates to adolescent mortality.
Code
# Create GDP bandsbins = [0, 1000, 5000, 10000, 50000, 1000000]labels = ['<1k', '1k-5k', '5k-10k', '10k-50k', '>50k']data['gdp_band'] = pd.cut(data['gdp per capita (constant 2015 us$)'], bins=bins, labels=labels)# Group and averagelatest_gender_data = data[(data['time_period'] == latest_year) & (data['sex'].isin(['Male', 'Female']))]avg_band_mortality = latest_gender_data.groupby(['gdp_band', 'sex'])['obs_value'].mean().reset_index()# Scatterplot by GDP Band( ggplot(avg_band_mortality) + aes(x='gdp_band', y='obs_value', color='sex') + geom_point(size=4) + geom_line(group='sex', size=1.5) + scale_color_manual(values={"Male": "#1f77b4", "Female": "#e377c2"}) + theme_minimal() + labs( title='Average Mortality Rate by GDP Band (Gender Split)', x='GDP Band', y='Average Mortality Rate (per 1,000)', color='Gender' ) + theme( plot_title=element_text(size=18, face="bold"), axis_title_x=element_text(size=14), axis_title_y=element_text(size=14), axis_text_x=element_text(size=12), axis_text_y=element_text(size=12), legend_title=element_text(size=14), legend_text=element_text(size=12) ))
Insight:
Mortality rates fall sharply once GDP per capita exceeds $10,000, with gender differences narrowing substantially in wealthier countries.
Conclusion
This analysis reveals persistent inequalities in adolescent mortality outcomes across genders, geographies, and income groups.
Sub-Saharan Africa remains the most affected region, while high-income countries enjoy lower adolescent mortality risks.
Recommendations: - Prioritize healthcare investments in low- and middle-income countries. - Strengthen gender-specific adolescent health interventions. - Foster global partnerships to improve health equity.
Investing in adolescent health is critical for achieving a healthier and more sustainable future globally.